Method and apparatus for automated analysis and identification of a person in image and video content

ABSTRACT

A method, apparatus, and computer readable medium for identifying a person in an image includes an image analyzer. The image analyzer determines the content of an image such as a person, location, and object shown in the image. A person in the image may be identified based on the content and event data stored in a database. Event data includes information concerning events and related people, locations, and objects determined from other images and information. Identification metadata is generated and linked to each analyzed image and comprises information determined during image analysis. Tags for images are generated based on identification metadata. The event database can be queried to identify particular people, locations, objects, and events depending on a user&#39;s request.

The present application is a continuation of prior application Ser. No.14/308,050 filed on Jun. 18, 2014, which is a continuation of priorapplication Ser. No. 13/207,974 filed on Aug. 11, 2011 and issued asU.S. Pat. No. 8,792,684 on Jul. 29, 2014, the disclosures of which areherein incorporated by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to visual identification, andmore particularly to identifying a person in an image or video.

BACKGROUND

Image acquisition devices such as video and still picture cameras allowusers to record images of various events. After images have beencaptured, the images are often loaded onto a local or remote storagedevice, such as a hard drive, for later retrieval. Since storage devicescan contain a large number of images, the identification andcategorization of images is a problem. Further, since a large number ofimages are stored, specific images of people, places, or objects arehard to locate.

Facial recognition can be used to detect people in images. However,facial recognition can fail to correctly identify people and also maynot be able to detect a person when the person's appearance changes.

SUMMARY

In one embodiment, a method for image analysis comprises determining thecontent of the image and identifying the person in the image based onthe content and event data. Identification metadata is generated afterthe person in the image is identified. The identification of the personin the image may additionally be based on existing metadata associatedwith the image. Determining content in the image may include determininga location depicted in an image and a preliminary identification of aperson in the image. In one embodiment, a notification is transmitted toa person identified in the image. Identification metadata associatedwith the image may be linked to the image. In another embodiment, a tagfor an image is generated based on metadata associated with the image.

These and other advantages of the general inventive concept will beapparent to those of ordinary skill in the art by reference to thefollowing detailed description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a system for identifying a person in an image;

FIG. 2 is a flowchart showing a method for use with the system of FIG.1;

FIG. 3 is a flowchart showing details of step 200 in FIG. 2;

FIG. 4 depicts the organization of an event table stored in an eventdatabase according to one embodiment;

FIG. 5 depicts the organization of a person table stored in the eventdatabase according to one embodiment; and

FIG. 6 is a high-level block diagram of a computer capable ofimplementing an image analyzer.

DETAILED DESCRIPTION

Described in an embodiment of the disclosed technology is a method andapparatus for identifying a person in an image. Images are analyzed byan image analyzer to determine content of an image. The content of animage can include a person, a location, an object, an environmentalcondition, etc. For example, an image may show a man (person) at theGrand Canyon (location) holding an umbrella (object) on a rainy day(environmental condition) as determined by facial, object, andenvironmental condition recognition. In this example, the content of theimage consists of a person, the Grand Canyon, an umbrella, and rain.Facial recognition can be used to make a preliminary identification ofperson in the image. The content of the image is then used inconjunction with event data stored in a database to identify the personin the image (or confirm the preliminary identification of the person inthe image determined using facial recognition). If the person in theimage is preliminarily identified as Bob Jones using facial recognition,this preliminary identification can be confirmed using determinedcontent in conjunction with event data stored in an event database. Theevent database contains a number of tables comprised of records ofevents, people, objects, etc. For example, event records can indicatethe date and time of an event, people who attended the event, thelocation of the event, and objects at the event. Person records cancontain information concerning where a person was at various dates andtimes, what the person was wearing at those dates and times, as well asvisual characteristics of the person at those dates and times. Theinformation in the event data can be entered by a user or determinedusing images retrieved from various sources and analyzed.

The preliminary identification of Bob Jones in the image can beconfirmed by determining if Bob Jones was at the Grand Canyon at a dateand time associated with the image (e.g., from metadata associated withthe image) using information contained in the event database. Ifinformation in the event database indicates that Bob Jones was at theGrand Canyon at the date and time associated with image, then thepreliminary identification of Bob Jones can be confirmed. Identificationmetadata indicating that Bob Jones is shown in the image is thengenerated and linked with the analyzed image.

FIG. 1 shows a schematic of a system in which one embodiment of a methodfor identifying people in images and video may be implemented. Userdevice 10 is an electronic device such as a personal computer, digitalcamera, personal digital assistant, smart phone, or cell phone. Userdevice 10 is in communication with image analyzer 14 via network 12which can be any type of wired or wireless network. Network 12 can varyin size from a local area network to a wide area network. Network 12, inone embodiment, is the Internet. User device 10 may alternatively beconnected directly to image analyzer 14. As shown in FIG. 1, imageanalyzer 14 is also in communication with content provider 16 vianetwork 12. Although only one content provider is shown, image analyzer14 can connect to multiple providers via network 12. Content provider 16may be one of various providers such as businesses or individualsproviding information to others via network 12. Although not shown inFIG. 1, image analyzer 14 can be connected via network 12 to additionalsources of information including both public sources such as businessesand private sources such as individuals. Image analyzer is incommunication with database 20 which stores images and associated datasuch as metadata and audio related to an image.

Image analyzer 14 analyzes images to identify people shown in theseimages. Image analyzer 14 uses event data stored in event database 20 inconjunction with content identified in a particular image to identifypeople shown in the particular image. Images for analysis may bereceived or retrieved from user device 10, network 12, or contentprovider 16. For example, a user may transmit an image for analysis fromuser device 10 via network 12 to image analyzer 14. Images may also beretrieved from user device 10, network 12, and content provider 16 via arequest from image analyzer 14. In one embodiment, image analyzer 14crawls network 12 searching for images for analysis. It should be notedthat an image may be one of a sequence of images in a video. Thus, videocan be analyzed as described herein similar to images but on a frame byframe or scene by scene basis.

FIG. 2 depicts a flowchart of a method for identifying a person in animage, according to one embodiment, for use with the system of FIG. 1. Aparticular image to be analyzed can be acquired in various ways such astransmitted to image analyzer 14 by a user via user device 10 orretrieved from network 12 or content provider 16 by image analyzer 14.At step 200 image analyzer 14 determines the content of the image. Thecontent of an image is what the image depicts. For example, the contentof an image is the people, place, objects, environmental conditions,etc. shown in the image. At step 202, image analyzer 14 identifies aperson in the image based on the content determined in step 200 andevent data, as described in further detail below. At step 204 imageanalyzer 14 generates identification metadata in response to determiningthe person in the image.

In one embodiment, the identification metadata associated with an imagecan be used to generate one or more tags for the image. Tagging ismetadata associated with an image and may be generated by a user. Tagscan also be generated by a computer by analyzing the images andassociated data. Tagging can be automated using the identificationmetadata generated using the method depicted in FIG. 2. Automatedtagging of images can be based on generated identification metadataalone or in conjunction with tags currently associated with an image.

FIG. 3 depicts a flowchart of steps performed when determining thecontent of an image in step 200 of FIG. 2. At step 300, image analyzer14 makes a preliminary identification of a person shown in the imageusing, in one embodiment, facial recognition. In addition, variousexpressions a person makes can be used to assist in identification (e.g.emotion detection). Other types of identification may be used such asbody recognition, activity or gait recognition, lip-based activityrecognition, iris recognition, fingerprint recognition, etc. dependingon what features of a person the image contains. Another type ofidentification can be based on the clothes a person wears frequently.

At step 302, image analyzer 304 determines an object shown in the image.An object is a thing or physical body. Object recognition is used in oneembodiment to identify objects depicted in an image. For example,vehicles, furniture, household items, and other objects may beidentified in an image using object recognition. Objects that are rareor unique may be additionally classified as landmarks (e.g. the EiffelTower or Cinderella's Castle).

At step 304, image analyzer 14 determines a location shown in the image.A location is a place shown in an image and, in many images, the placethe image was captured. Environmental features (e.g., scenery) andobjects determined to be shown in an image may be used to determine thelocation shown in an image. For example, scenery such as grassy rollinghills, sand dunes, jungle, city, or other scenery may provide clues asto the location shown in an image. Objects and landmarks determined tobe shown in an image may also be used to determine the location shown inthe image. Although well known landmarks such as the Eiffel Tower, MountRushmore, or the Statue of Liberty may enable image analyzer 14 todetermine a location with a relatively high level of confidence, otherless famous “landmarks” can be used to determine a location as well. Forexample, a person's house may be distinctive enough to determine alocation shown in an image with a certain level of confidence. One ormore environmental features and objects may be identified and used inthe determination of a location shown in an image.

At step 306, image analyzer determines environmental conditions shown inthe image. Environmental conditions include weather and evidence ofrecent weather (e.g., snow). For example, the weather shown in an imagesuch as rain, sleet, or snow, may be identified. In addition, otherenvironmental conditions, such as the position of the sun or stars shownin an image, may be identified in addition to weather conditions.

Additional steps for determining other types of content in an image notshown in FIG. 3 may be used as well. For example, text contained in animage may be recognized and interpreted using text recognition. Theclothing a person is wearing, the person's hair color and length, andother potential identifying characteristics and features may bedetermined as well. Other potential identifying characteristics andfeatures include emotion detection, the origin of the video or photo(i.e., who took it and where), email addresses of whoever sent it (or towhomever it was sent), the age of the image (when it was taken), theages of the people in the image, and audio information in the video thatcontains the image.

It should be noted that the determinations in the steps of FIG. 3, aswell as determinations of other types of content shown in an image, maynot result in accurate identifications. To account for this potentialinaccuracy, a level of confidence concerning the accuracy of adetermination can be generated and associated with each determination.For example, a confidence level of 1 to 100 can be associated with adetermination. If a determination is made in which multiple points ofdata correspond, then a high confidence level such as 90 may beassociated with the determination. If a determination is made in whichonly a few points of data correspond, then a low confidence level suchas 10 may be associated with the determination. The determination andtheir associated levels of confidence can be used to aid in identifyingthe person in the image and the content of the image as describedfurther below.

Existing metadata is often associated with an image captured using acamera or other device. Existing metadata can indicate the date theimage was captured, the time of capture, and in some cases, additionalinformation such as the camera make and model, and camera setting usedfor the photo. In some cases, existing metadata may also includegeographic information indicating the location of the camera when theimage was captured. Existing metadata may be associated with an imageusing a standard such as Exchangeable Image File Format (EXIF) or otherstandard. Existing metadata can be used to assist in determining thecontent of an image.

Content of the image determined in step 200 of FIG. 2 and described insteps 300-306 of FIG. 3 is then used in conjunction with event data toidentify the person in the image at step 202 of FIG. 2.

Event data consists of information concerning a particular event,people, objects, locations, environmental conditions, etc. Event data isgenerated by analyzing images received from users and retrieved fromvarious sources such as network 12 and content provider 16. Event datamay also be generated by a user entering data. Event data is stored inevent database 20, in one embodiment, as a collection of tables whichcan be accessed by image analyzer 14. A particular event can beconsidered a specific time or a range of times in which somethingoccurred. For example, an event may have occurred on Jan. 1, 2010 at1:01 am or other date and time. Particular events may have a specificduration such as a few seconds (e.g., sports play), a few minutes (e.g.,a child's recital), or hours (e.g., a wedding).

FIG. 4 depicts an example of how event data corresponding to particularevents may be stored as an event table in database 20 according to oneembodiment. Each event record 400, 402, 404, 406, etc. has a uniqueidentification number 410. Each event record 400-406 has multiple fieldsassociated with it, for example, date 412, time 414, event title 416,location 418, people present 420, objects present 422, landmarks 424,environmental conditions 426, etc. Additional fields may be added tofurther define events (such as a time zone). As described above, data inthe event table of FIG. 4 may be generated by a user entering data orfrom images acquired and analyzed by image analyzer 14.

Event database 20 also stores tables concerning specific people,objects, locations, and environmental conditions. FIG. 5 depicts anexample of a person table in which data concerning people may be stored.Each person record 500, 502, 504, etc. has a unique person ID 510. Eachperson record 500-504 has multiple fields associated with it, forexample, name 512, date 514, time 516, pants 518, shirt 520, shoes 522,hat 524, eyewear 526, hair color 528, and hair length 530. Additionalfields may be added to further define a specific person at a specificdate and time (such as a time zone). Similar to the event table of FIG.4, the data in the person table of FIG. 5 may be generated by a userentering data or from images acquired and analyzed by image analyzer 14.

Event data can be generated based on images previously acquired fromvarious sources. For example, event data can be generated by analysis ofvarious images obtained from content provider 16. An individual'scollection of photos located on user device 10 may be used to generateevent data. Additional information can be obtained from other sourcessuch as contact lists, email addresses, calendars, notes, public eventannouncements, social networking linkages between friends and family,personal call history, frequent co-location of one or more people asdetermined by location information form global positioning services(GPSs), etc.

Returning to FIG. 2, at step 202, the content determined in the imageand the event data are used to identify a person in an image in avariety of ways.

A preliminary identification of a person based on image content usingfacial recognition can be confirmed using event data. For example, if aperson in an image is preliminarily identified as Bob Jones, event datacan be used to confirm the preliminary identification. Other types ofcontent determined to be shown in the image preliminarily identifyingBob Jones, such as the location depicted in the image, can be comparedto event data stored in database 20. If, for example, the locationdepicted in the image is Bob Jones' house and event data stored indatabase 20 indicates that Bob Jones is known to have been in his houseat the date and time of the image, then it is reasonably probable thatthe preliminary identification of Bob Jones in the image is correct andthus confirmed with a level of confidence corresponding to a level ofconfidence associated with the related event data.

Other types of event data may also be used to confirm the preliminaryidentification of Bob Jones in the image. For example, if contentanalysis of an image preliminarily identifies Bob Jones as a person inthe image, event data as shown in FIG. 5 can be used to confirm thepreliminary identification with an increased level of confidence. If BobJones is identified as wearing certain clothes in the image, the clothesworn by Bob Jones at the date and time of the image can be checkedagainst information stored in one or more records such as the recordsshown in FIG. 5. If the identification of the clothes worn by Bob Jonesin the image match the clothes identified in event data as being worn byBob Jones at approximately the same date and time as when the image wascaptured, then the preliminary identification of Bob Jones as the personin the image can be confirmed with a level of confidence correspondingto the level confidence associated with the record in database 20. Inone embodiment, probability distribution or interpolated methodology maybe used to identify people with varying probability. A classifier can beused for different aspects/features separately or collectively/inconjunction. For example, an exact match of the clothes a person iswearing may not be needed to identify the person. If the person beingidentified is wearing similar clothes in all previous images, then analgorithm can be used to classify with a certain probability a match ofthe person in the image with the same person wearing different clothesin previous images.

In some images, not all of the people in the image may be recognized bycontent analysis (i.e., facial recognition). For example, if four peopleare determined to be in an image and only three are capable of beingpreliminarily identified using facial recognition, the identity of thefourth person may be determined using event data. If the image isdetermined to be associated with an event in which four people arepresent, this information can be used to identify the unknown fourthperson shown in the image. Additional event data such as the clothes thefourth person is known to be wearing at the approximate date and time ofthe image may be used to confirm the identification of the fourth personwith a level of confidence based on the additional event data used tomake the identification. Additionally, if the forth person can beidentified in another picture taken at the same event, features like theclothes that the person is wearing can be used to identify the person inanother photo in which his/her face is not sufficiently visible to beidentified.

It should be noted that event data can be used to determine that apreliminary identification of a person in an image is incorrect. Forexample, content analysis of an image using facial recognition mayresult in a preliminary identification of a person as a particularindividual at a particular location. Event data may indicate that theparticular individual was not at the location identified in the image,and thus, the preliminary identification is incorrect. For example,location data or calendar data could indicate that the person is at adifferent location or event. Additional event data may then be used todetermine the identification of the person in the image.

Additional information associated with an image can be used to determinethe content of the image. For example, since video is comprised of asequence of images, each image, or a selected subset of images, of avideo may be analyzed to determine the content of the image. Theinformation obtained by this analysis of a sequence of images can beused to determine the content of each particular image. For example,each image in a sequence of images in a video may not depict people orobjects completely as they may be cut out or occluded as they move outof frame in the sequence. The people or objects cut off in one image maybe determined using images that occur before and after the current imagethat may contain complete depictions of the cut off person or object.

Audio associated with an image or video may also be analyzed usingspeech recognition, speaker identification, natural language processing,or the classification of environmental sounds (e.g., street, forest,desert, etc.) for use in determining the content of a related image. Forexample, a person's voiceprint may be identified and associated with aperson shown in an image or video thereby identifying the person.

At step 204 of FIG. 2, image analyzer 14 generates identificationmetadata in response to identifying the person in the image. Theidentification metadata can be linked and stored with the related image.For example, an image and the associated metadata can be stored in thesame file or location (e.g. EXIF data). The identification metadata canalso be stored separate from the image with information identifying theimage to which the identification metadata pertains. For example,identification metadata can be associated with a unique identifier thatidentifies a particular image.

The identification metadata generated at step 204 of FIG. 2 canadditionally be used to add to the data contained in event database 20.For example, a person identified in an image together with informationassociated with the image can be used to created new records in thetables of FIGS. 4 and 5 as well as additional tables as appropriate.

Event data can be used to provide users with various images of aparticular person, object, location, etc. For example, numerous imagesof a particular person can be provided to a user through user device 10by querying database 20 via network 12 and image analyzer 14. Inaddition, images of a particular person over a particular period of timecan be provided to a user. Various combinations of identifyinginformation may be used to provide images to a user. For example, a usercan query database 20 for a particular person at a particular locationand be provided with one or more images based on the query.

A series of images of a particular person can be arranged forpresentation based on event data associated with the particular person.For example, one image per year can be selected to form a sequence ofimages showing a particular person over time. In addition, event dataallows identification and retrieval of images of a particular person ata particular time. For example, an image of a particular person 20 yearsago can be determined using event data and then provided to a user.

In one embodiment, a particular person can be notified when an imagedepicting them is found. For example, image analyzer 14 can beconfigured to search various sources such as content provider 16, web18, and one or more user devices such as user device 10 to acquire andanalyze images. Image analyzer 14 can transmit a notification to a userwhen a particular person is identified in a new image. An image in whichperson is identified may be cropped and/or zoomed in to the particularperson identified.

In one embodiment, event data stored in event database 20 can be used toidentify images of a person at a specific time. For example, a user canquery event database 20 to identify images of a person 20 years ago.Images of the particular person having dates approximately 20 yearsprior to the current date can be identified and presented to a user. Auser can also query event database 20 to identify pictures of locations,objects, people, etc. as well as combinations of elements. For example,a user can query event database 20 for images of a particular person ata particular location including certain objects.

It should be noted that some users may be authorized to update orcorrect information contained in event database 20. For example, if theidentification of people, objects, or locations is considered incorrectby a particular user, that user may be able to correct theidentification of the particular person, object, or location. Eventdatabase 20 may be updated immediately to correct the information or itmay be corrected after multiple users have requested the samecorrection.

It should be noted that the methods of identification described abovecan be used to determine content of images that are being transmitted insubstantially real time such as video broadcasts and video conferencing.Identifications of people, locations, and objects, made during analysiscan be used to augment the information contained in the video. Forexample, people shown in a video can be identified as the video is beingshown and an overlay of identification information can be provided to aviewer.

In one embodiment, the identification of people, locations and objectsin images can be augmented using information contained in communicationsassociated with the images. In one embodiment, text of emails containingimages can be scanned to determine if the text provides additionalidentification information concerning the images. In this embodiment,text in an email such as “Pictures of Jon Vanhass at the beach” can beused to augment the identification of people in the attached images aswell as the location shown in the images. In addition, this informationcan be stored in event database 20 for use in later identifications.

In another embodiment, information from event data could be stored indatabase 20 and used to estimate social relationships between peoplefrom many images. As different people are identified using the contentof images and event data about those people and images are stored, thesystem can begin to estimate social relationships between individuals.These relationships would have stronger or weaker computed links basedon the co-occurrence of people (420 of FIG. 4) and event data from thecorresponding images that they were identified in. For example, as ZoeEllen and Nathanial Jackson appeared in more images together indifferent locations, the strength of a computed a social link could beincremented and the system might propose that Zoe and Nathanial arefriends or are related. Similarly, if additional links between Zoe Ellenand Mary Ellen as well as Byron Ellen and Mary Ellen could beformulated. On subsequent computations, the system could then propose aweak link between Zoe and Byron or Nathanial and Mary. In an additionalembodiment, social links determined as friends computed with theaforementioned technique could be promoted to stronger social linkindicating family if other personal attributes like hair color (528 ofFIG. 5), hair length (530 of FIG. 5), time (516 of FIG. 5), etc. fromthe event database 20 are considered as factual input to a statisticalclassification routine.

Image analyzer 14 may be implemented on a computer to perform themethods of FIGS. 2 and 3. A high-level block diagram of such a computeris illustrated in FIG. 6. Computer 602 contains a processor 604 whichcontrols the overall operation of the computer 602 by executing computerprogram instructions which define such operation. The computer programinstructions may be stored in a storage device 612, or other computerreadable medium (e.g., magnetic disk, CD ROM, etc.), and loaded intomemory 610 when execution of the computer program instructions isdesired. Thus, the method steps of FIGS. 2 and 3 can be defined by thecomputer program instructions stored in the memory 610 and/or storage612 and controlled by the processor 604 executing the computer programinstructions. For example, the computer program instructions can beimplemented as computer executable code programmed by one skilled in theart to perform an algorithm defined by the method steps of FIGS. 2 and3. Accordingly, by executing the computer program instructions, theprocessor 1004 executes an algorithm defined by the method steps of FIG.3. The computer 602 also includes one or more network interfaces 606 forcommunicating with other devices via a network. The computer 602 alsoincludes input/output devices 608 that enable user interaction with thecomputer 602 (e.g., display, keyboard, mouse, speakers, buttons, etc.)One skilled in the art will recognize that an implementation of anactual computer could contain other components as well, and that FIG. 6is a high level representation of some of the components of such acomputer for illustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the general inventive concept herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present inventive concept and thatvarious modifications may be implemented by those skilled in the artwithout departing from the scope and spirit of the general inventiveconcept. Those skilled in the art could implement various other featurecombinations without departing from the scope and spirit of the generalinventive concept.

The invention claimed is:
 1. A method comprising: establishing, by animage server comprising a processor, an identification of a persondepicted in a first digital image based on a first portion of the personbeing depicted in the first digital image; generating, by the imageserver, a confidence level associated with the identification;confirming, by the image server, the identification of the persondepicted in the first digital image based on: a same geographic locationbeing depicted in the first digital image and a second digital image,the geographic location determined by the image server based onenvironmental features identified by the image server in the firstdigital image and the second digital image; and the second digital imagedepicting a second portion of the person; and increasing, by the imageserver, the confidence level associated with the identification based onthe confirming.
 2. The method of claim 1, wherein the confirming isfurther based on the geographic location being associated with theperson in event data associated with the person.
 3. The method of claim2, further comprising: receiving input associating the geographiclocation with the person.
 4. The method of claim 1, further comprising:generating identification metadata in response to confirming theidentification of the person depicted in the first digital image.
 5. Themethod of claim 4, further comprising: generating a tag for the firstdigital image based on the identification metadata.
 6. The method ofclaim 1, wherein the geographic location determined by the image serveris further based on a landmark identified by the image server in thefirst digital image and the second digital image.
 7. The method of claim1, wherein the confirming the identification of the person depicted inthe first digital image is further based on metadata associated with thefirst digital image.
 8. An apparatus comprising: a processor; and amemory that stores executable instructions that, when executed by theprocessor, facilitate performance of operations comprising:preliminarily identifying a person depicted in a first digital imagebased on a first portion of the person being depicted in the firstdigital image, to generate a preliminary identification; determining aconfidence level associated with the preliminary identification;confirming the preliminary identification of the person depicted in thefirst digital image based on: event data associated with the person; anda same geographic location being depicted in the first digital image anda second digital image, the second digital image depicting a secondportion of the person, the geographic location determined based onenvironmental features identified in the first digital image and thesecond digital image; and increasing the confidence level associatedwith the preliminary identification based on the confirming.
 9. Theapparatus of claim 8, wherein the confirming is further based on thegeographic location being associated with the person in the event dataassociated with the person.
 10. The apparatus of claim 9, the operationsfurther comprising: receiving input associating the geographic locationwith the person.
 11. The apparatus of claim 8, the operations furthercomprising: generating identification metadata in response to confirmingthe identification of the person depicted in the first digital image.12. The apparatus of claim 11, the operations further comprising:generating a tag for the first digital image based on the identificationmetadata.
 13. The apparatus of claim 8, wherein the geographic locationis determined further based on a landmark identified in the firstdigital image and the second digital image.
 14. The apparatus of claim8, wherein the confirming the identification of the person depicted inthe first digital image is further based on metadata associated with thefirst digital image.
 15. A non-transitory machine-readable storagemedium, comprising executable instructions that, when executed by aprocessor, facilitate performance of operations, comprising:establishing a preliminary identification of a person depicted in afirst digital image based on a first portion of the person beingdepicted in the first digital image; generating a confidence levelassociated with the preliminary identification; confirming thepreliminary identification of the person depicted in the first digitalimage based on: a same geographic location being depicted in the firstdigital image and a second digital image, the geographic locationdetermined based on environmental features identified in the firstdigital image and the second digital image; and the second digital imagedepicting a second portion of the person; and increasing the confidencelevel associated with the preliminary identification based on theconfirming and event data associated with the person.
 16. Thenon-transitory machine-readable storage medium of claim 15, wherein theconfirming is further based on the geographic location being associatedwith the person in event data associated with the person.
 17. Thenon-transitory machine-readable storage medium of claim 16, theoperations further comprising: receiving input associating thegeographic location with the person.
 18. The non-transitorymachine-readable storage medium of claim 15, the operations furthercomprising: generating identification metadata in response to confirmingthe identification of the person depicted in the first digital image.19. The non-transitory machine-readable storage medium of claim 18, theoperations further comprising: generating a tag for the first digitalimage based on the identification metadata.
 20. The non-transitorymachine-readable storage medium of claim 15, wherein the geographiclocation is determined further based on a landmark identified in thefirst digital image and the second digital image.